ScRNAseq analysis
Library 2 pre-processing
Juba
Compiled on: 2025-07-26
Import the data
## An object of class Seurat
## 51279 features across 4197 samples within 3 assays
## Active assay: SCT (14676 features, 2000 variable features)
## 3 layers present: counts, data, scale.data
## 2 other assays present: RNA, HTO
## 2 dimensional reductions calculated: pca, umapHere’re some representation
General conditions
Clusteres at res0.1
Clonotypes distributions
Cell cycle distribution
Cells inspection
I’ll inspect the expression of some markers to determine the cell types
This will help us determine any cluster that may not be of interest ! (clusters at res 0.1)
T lymphocytes
Other cell types
Basically here, we’re looking for
- Monocytes & macrophages (CD14, LYZ)
- NK cells (NKG7, GNLY)
- B Lymphocytes (CD19, CD27)
As for the feature plots :
Clonotypes inspection
Now we’ll inspect the distribution of the clonotypes
Let’s group unique and low-count clones together
Now let’s see the distribution among the conditions and the clusters
Clones among conditions
Clones among clusters
Filtering cells
Now let’s filter unwanted cells
# define cells to remove :
# ---------------------- :
cells_rm <- WhichCells(
SeuratObj,
expression = umap_1 > 5 |
Clonotype %in% c("unique","low_count")
)
# subset seurat object :
# -------------------- :
SeuratObj_filt <- subset(SeuratObj, cells = setdiff(Cells(SeuratObj),cells_rm))
# rejoin conditions and clusters :
# ------------------------------ :
SeuratObj_filt@meta.data <- SeuratObj_filt@meta.data %>%
mutate(MyClusters = case_when(
umap_2 < -1 & Condition == "Relapse" ~ "R",
umap_2 < -1 & Condition == "Diagnosis" ~ "D_in_R",
umap_2 > -1 & Condition == "Relapse" ~ "R_in_D",
umap_2 > -1 & Condition == "Diagnosis" ~ "D"
))
# plot :
# ---- :
DimPlot(SeuratObj_filt,
group.by = "MyClusters",
cols = c(MyPalette, colors(distinct = T)),
pt.size = 1,
alpha = .7,
label = T,
label.box = T,
label.color = "#fff",
repel = T)+
My_umap_theme()+
scale_fill_manual(values = MyPalette)
Set a threshold of 30 cells minimum to be kept for further analysis (so any cluster below 30 will be filtered again)
Removing Rel_in_Diag & Diag_in_Rel
TCR inspections
Now, let’s inspect the VDJ expression :
- Among our conditions
- Among the clonotypes
Cell cycle inspection
Now, let’s inspect the cell cycle of the TCRs :
Now, let’s inspect the cell cycle of the clonotypes :
Now, let’s dive deeper
We’ll inspect the cell cycle within the 3 major clonotypes (cl1, cl2, cl3) against each TCR
Clonotype 1
Clonotype 2
Clonotype 3
Analysis duration : 51s
Time : 12:54:49
Session info
## ─ Session info ───────────────────────────────────────────────────────────────
## setting value
## version R version 4.4.2 (2024-10-31 ucrt)
## os Windows 11 x64 (build 26100)
## system x86_64, mingw32
## ui RTerm
## language (EN)
## collate French_France.utf8
## ctype French_France.utf8
## tz Europe/Paris
## date 2025-07-26
## pandoc 3.2 @ C:/Program Files/RStudio/resources/app/bin/quarto/bin/tools/ (via rmarkdown)
## quarto NA @ C:\\PROGRA~1\\RStudio\\RESOUR~1\\app\\bin\\quarto\\bin\\quarto.exe
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